科研信息化技术与应用
科研信息化技術與應用
과연신식화기술여응용
E-science Technology & Application
2015年
2期
66-73
,共8页
刘仰东%田野%袁博%毛伟
劉仰東%田野%袁博%毛偉
류앙동%전야%원박%모위
流量模型%等待时间%打车热点
流量模型%等待時間%打車熱點
류량모형%등대시간%타차열점
taxi trafifc lfow%waiting time%hotspots
作为城市交通的基础设施,出租车在日常交通中起着重要作用.随着城市规模的扩大,人们的出行需求不断增加,然而出租车的分布与叫车需求分布之间难以匹配,从而导致打车困难问题的出现,这一现象在大型城市尤其明显.造成打车难的主要原因在于司乘之间信息不能互通,GPS、车联网等技术能够提供车辆位置、运行轨迹等信息,通过对这些信息数据进行处理,可以获得有价值的信息,将其提供至司乘双方能够提升出租车运营效率.现有信息处理方法较为简单,忽略了较多关键影响因素,难以达到理想的效果.因此,本文提出了一种基于出租车轨迹和路网数据来衡量打车难度的出租车流量模型,并通过综合时间、天气等因素对模型进行优化,提升了模型的实用性.基于该模型利用数据挖掘算法抽取有用信息,提供给出租车司机和乘客.最后,本文基于实际出租车数据对模型进行实验验证,结果证明了模型的有效性及实用性.
作為城市交通的基礎設施,齣租車在日常交通中起著重要作用.隨著城市規模的擴大,人們的齣行需求不斷增加,然而齣租車的分佈與叫車需求分佈之間難以匹配,從而導緻打車睏難問題的齣現,這一現象在大型城市尤其明顯.造成打車難的主要原因在于司乘之間信息不能互通,GPS、車聯網等技術能夠提供車輛位置、運行軌跡等信息,通過對這些信息數據進行處理,可以穫得有價值的信息,將其提供至司乘雙方能夠提升齣租車運營效率.現有信息處理方法較為簡單,忽略瞭較多關鍵影響因素,難以達到理想的效果.因此,本文提齣瞭一種基于齣租車軌跡和路網數據來衡量打車難度的齣租車流量模型,併通過綜閤時間、天氣等因素對模型進行優化,提升瞭模型的實用性.基于該模型利用數據挖掘算法抽取有用信息,提供給齣租車司機和乘客.最後,本文基于實際齣租車數據對模型進行實驗驗證,結果證明瞭模型的有效性及實用性.
작위성시교통적기출설시,출조차재일상교통중기착중요작용.수착성시규모적확대,인문적출행수구불단증가,연이출조차적분포여규차수구분포지간난이필배,종이도치타차곤난문제적출현,저일현상재대형성시우기명현.조성타차난적주요원인재우사승지간신식불능호통,GPS、차련망등기술능구제공차량위치、운행궤적등신식,통과대저사신식수거진행처리,가이획득유개치적신식,장기제공지사승쌍방능구제승출조차운영효솔.현유신식처리방법교위간단,홀략료교다관건영향인소,난이체도이상적효과.인차,본문제출료일충기우출조차궤적화로망수거래형량타차난도적출조차류량모형,병통과종합시간、천기등인소대모형진행우화,제승료모형적실용성.기우해모형이용수거알굴산법추취유용신식,제공급출조차사궤화승객.최후,본문기우실제출조차수거대모형진행실험험증,결과증명료모형적유효성급실용성.
As an infrastructure of urban transport, taxis play an important role in everyday traffic. With the expansion of the city, people's travel demand continues to increase.However, it is difficult to match the distribution of taxis with the distribution of demand, which results in the difficulty problem of taking taxi, especially in large cities. The main reason causing difficulty of taking taxi is that the information cannot be exchanged between drivers and passengers.Current technology such as GPS and Internet of vehicles can provide vehicle location, running track and other information.Valuable information can be generated through data processing, by which drivers and passengers can enhance their travel efficiency. Existing data processing method is not efficient enough because of ignoring some key factors. This paper presents a taxi traffic model based on the taxi track and road network data to measure the difficulty of a taking a vacant taxi. Then we optimize the model by considering the other factors such as time and weather. We use data mining algorithms to extract useful information in the proposed model and then providing these valuable information to the taxi drivers and passengers. At last, we verify the model by experiments based on actual data of taxis and the results demonstrate the effectiveness and practicality of the proposed model.